def main():
"""
Command line interface to import a model in the PEtab
(https://github.com/PEtab-dev/PEtab/) format into AMICI.
"""
args = parse_cli_args()
if args.yaml_file_name:
pp = petab.Problem.from_yaml(args.yaml_file_name)
else:
pp = petab.Problem.from_files(
sbml_file=args.sbml_file_name,
condition_file=args.condition_file_name,
measurement_file=args.measurement_file_name,
parameter_file=args.parameter_file_name,
observable_files=args.observable_file_name)
# First check for valid PEtab
petab.lint_problem(pp)
if args.flatten:
petab.flatten_timepoint_specific_output_overrides(pp)
import_model(model_name=args.model_name,
sbml_model=pp.sbml_model,
condition_table=pp.condition_df,
observable_table=pp.observable_df,
measurement_table=pp.measurement_df,
model_output_dir=args.model_output_dir,
compile=args.compile,
verbose=args.verbose)
def __init__(self, pysb_model: 'pysb.Model' = None, *args, **kwargs):
"""
Constructor
:param pysb_model: PySB model instance for this PEtab problem
:param args: See :meth:`petab.Problem.__init__`
:param kwargs: See :meth:`petab.Problem.__init__`
"""
flatten = kwargs.pop('flatten', False)
super().__init__(*args, **kwargs)
if flatten:
petab.flatten_timepoint_specific_output_overrides(self)
self.pysb_model: 'pysb.Model' = pysb_model
self._add_observation_model()
if self.pysb_model is not None:
self.sbml_document, self.sbml_model = \
create_dummy_sbml(
self.pysb_model,
observable_ids=self.observable_df.index.values
if self.observable_df is not None else None
)
def test_flatten_timepoint_specific_output_overrides_special_cases():
"""Test flatten_timepoint_specific_output_overrides
for special cases:
* no preequilibration
* no observable parameters
"""
observable_df = pd.DataFrame(data={
OBSERVABLE_ID: ['obs1'],
OBSERVABLE_FORMULA: ['species1'],
NOISE_FORMULA: ['noiseParameter1_obs1']
})
observable_df.set_index(OBSERVABLE_ID, inplace=True)
observable_df_expected = pd.DataFrame(data={
OBSERVABLE_ID: ['obs1__noiseParOverride1__condition1',
'obs1__noiseParOverride2__condition1'],
OBSERVABLE_FORMULA: [
'species1',
'species1'],
NOISE_FORMULA: ['noiseParameter1_obs1__noiseParOverride1__condition1',
'noiseParameter1_obs1__noiseParOverride2__condition1']
})
observable_df_expected.set_index(OBSERVABLE_ID, inplace=True)
# Measurement table with timepoint-specific overrides
measurement_df = pd.DataFrame(data={
OBSERVABLE_ID:
['obs1', 'obs1', 'obs1', 'obs1'],
SIMULATION_CONDITION_ID:
['condition1', 'condition1', 'condition1', 'condition1'],
TIME:
[1.0, 1.0, 2.0, 2.0],
MEASUREMENT:
[.1] * 4,
NOISE_PARAMETERS:
['noiseParOverride1', 'noiseParOverride1',
'noiseParOverride2', 'noiseParOverride2'],
})
measurement_df_expected = pd.DataFrame(data={
OBSERVABLE_ID:
['obs1__noiseParOverride1__condition1',
'obs1__noiseParOverride1__condition1',
'obs1__noiseParOverride2__condition1',
'obs1__noiseParOverride2__condition1'],
SIMULATION_CONDITION_ID:
['condition1', 'condition1', 'condition1', 'condition1'],
TIME:
[1.0, 1.0, 2.0, 2.0],
MEASUREMENT:
[.1] * 4,
NOISE_PARAMETERS:
['noiseParOverride1', 'noiseParOverride1',
'noiseParOverride2', 'noiseParOverride2'],
})
problem = petab.Problem(measurement_df=measurement_df,
observable_df=observable_df)
assert petab.lint_problem(problem) is False
# Ensure having timepoint-specific overrides
assert petab.lint.measurement_table_has_timepoint_specific_mappings(
measurement_df) is True
petab.flatten_timepoint_specific_output_overrides(problem)
# Timepoint-specific overrides should be gone now
assert petab.lint.measurement_table_has_timepoint_specific_mappings(
problem.measurement_df) is False
assert problem.observable_df.equals(observable_df_expected) is True
assert problem.measurement_df.equals(measurement_df_expected) is True
assert petab.lint_problem(problem) is False
problem = petab.Problem.from_yaml(yaml_file)
elif model_type == "pysb":
import pysb
pysb.SelfExporter.cleanup()
pysb.SelfExporter.do_export = True
case_dir = os.path.join(petabtests.PYSB_DIR, case)
# import petab problem
yaml_file = os.path.join(case_dir, petabtests.problem_yaml_name(case))
problem = PysbPetabProblem.from_yaml(yaml_file,
flatten=case.startswith('0006'))
else:
raise ValueError(f"Unsupported model_type: {model_type}")
# compile amici model
if case.startswith('0006') and model_type != "pysb":
petab.flatten_timepoint_specific_output_overrides(problem)
model_output_dir = f'amici_models/model_{case}'
model = import_petab_problem(problem,
model_output_dir=model_output_dir,
force_compile=True)
# simulate
ret = simulate_petab(problem, model, log_level=logging.DEBUG)
rdatas = ret['rdatas']
chi2 = sum(rdata['chi2'] for rdata in rdatas)
llh = ret['llh']
simulation_df = rdatas_to_measurement_df(rdatas, model,
problem.measurement_df)
petab.check_measurement_df(simulation_df, problem.observable_df)
simulation_df = simulation_df.rename(
def test_flatten_timepoint_specific_output_overrides():
"""Test flatten_timepoint_specific_output_overrides"""
observable_df = pd.DataFrame(data={
OBSERVABLE_ID: ['obs1'],
OBSERVABLE_FORMULA: [
'observableParameter1_obs1 + observableParameter2_obs1'],
NOISE_FORMULA: ['noiseParameter1_obs1']
})
observable_df.set_index(OBSERVABLE_ID, inplace=True)
observable_df_expected = pd.DataFrame(data={
OBSERVABLE_ID: ['obs1_1', 'obs1_2', 'obs1_3'],
OBSERVABLE_FORMULA: [
'observableParameter1_obs1_1 + observableParameter2_obs1_1',
'observableParameter1_obs1_2 + observableParameter2_obs1_2',
'observableParameter1_obs1_3 + observableParameter2_obs1_3'],
NOISE_FORMULA: ['noiseParameter1_obs1_1',
'noiseParameter1_obs1_2',
'noiseParameter1_obs1_3']
})
observable_df_expected.set_index(OBSERVABLE_ID, inplace=True)
# Measurement table with timepoint-specific overrides
measurement_df = pd.DataFrame(data={
OBSERVABLE_ID:
['obs1', 'obs1', 'obs1', 'obs1'],
SIMULATION_CONDITION_ID:
['condition1', 'condition1', 'condition1', 'condition1'],
PREEQUILIBRATION_CONDITION_ID:
['', '', '', ''],
TIME:
[1.0, 1.0, 2.0, 2.0],
MEASUREMENT:
[np.nan] * 4,
OBSERVABLE_PARAMETERS:
['obsParOverride1;1.0', 'obsParOverride2;1.0',
'obsParOverride2;1.0', 'obsParOverride2;1.0'],
NOISE_PARAMETERS:
['noiseParOverride1', 'noiseParOverride1',
'noiseParOverride2', 'noiseParOverride2']
})
measurement_df_expected = pd.DataFrame(data={
OBSERVABLE_ID:
['obs1_1', 'obs1_2', 'obs1_3', 'obs1_3'],
SIMULATION_CONDITION_ID:
['condition1', 'condition1', 'condition1', 'condition1'],
PREEQUILIBRATION_CONDITION_ID:
['', '', '', ''],
TIME:
[1.0, 1.0, 2.0, 2.0],
MEASUREMENT:
[np.nan] * 4,
OBSERVABLE_PARAMETERS:
['obsParOverride1;1.0', 'obsParOverride2;1.0',
'obsParOverride2;1.0', 'obsParOverride2;1.0'],
NOISE_PARAMETERS:
['noiseParOverride1', 'noiseParOverride1',
'noiseParOverride2', 'noiseParOverride2']
})
problem = petab.Problem(measurement_df=measurement_df,
observable_df=observable_df)
assert petab.lint_problem(problem) is False
# Ensure having timepoint-specific overrides
assert petab.lint.measurement_table_has_timepoint_specific_mappings(
measurement_df) is True
petab.flatten_timepoint_specific_output_overrides(problem)
# Timepoint-specific overrides should be gone now
assert petab.lint.measurement_table_has_timepoint_specific_mappings(
problem.measurement_df) is False
assert problem.observable_df.equals(observable_df_expected) is True
assert problem.measurement_df.equals(measurement_df_expected) is True
assert petab.lint_problem(problem) is False
def test_flatten_timepoint_specific_output_overrides(minimal_sbml_model):
document, model = minimal_sbml_model
petab.sbml.add_global_parameter(
sbml_model=model, parameter_id='observableParameter1_obs1')
petab.sbml.add_model_output_with_sigma(
sbml_model=model, observable_id='obs1',
observable_formula='observableParameter1_obs1')
# Measurement table with timepoint-specific overrides
measurement_df = pd.DataFrame(data={
'observableId':
['obs1', 'obs1', 'obs1', 'obs1'],
'simulationConditionId':
['condition1', 'condition1', 'condition1', 'condition1'],
'preequilibrationConditionId':
['', '', '', ''],
'time':
[1.0, 1.0, 2.0, 2.0],
'measurement':
[np.nan] * 4,
'observableParameters':
['obsParOverride1', 'obsParOverride2',
'obsParOverride2', 'obsParOverride2'],
'noiseParameters':
['noiseParOverride1', 'noiseParOverride1',
'noiseParOverride2', 'noiseParOverride2']
})
measurement_df_expected = pd.DataFrame(data={
'observableId':
['obs1_1', 'obs1_2', 'obs1_3', 'obs1_3'],
'simulationConditionId':
['condition1', 'condition1', 'condition1', 'condition1'],
'preequilibrationConditionId':
['', '', '', ''],
'time':
[1.0, 1.0, 2.0, 2.0],
'measurement':
[np.nan] * 4,
'observableParameters':
['obsParOverride1', 'obsParOverride2',
'obsParOverride2', 'obsParOverride2'],
'noiseParameters':
['noiseParOverride1', 'noiseParOverride1',
'noiseParOverride2', 'noiseParOverride2']
})
problem = petab.Problem(sbml_model=model,
measurement_df=measurement_df)
assert petab.lint_problem(problem) is False
# Ensure having timepoint-specific overrides
assert petab.lint.measurement_table_has_timepoint_specific_mappings(
measurement_df) is True
petab.flatten_timepoint_specific_output_overrides(problem)
# Timepoint-specific overrides should be gone now
assert petab.lint.measurement_table_has_timepoint_specific_mappings(
problem.measurement_df) is False
assert problem.measurement_df.equals(measurement_df_expected) is True
assert petab.lint_problem(problem) is False
def main():
"""Simulate the model specified on the command line"""
args = parse_cli_args()
if args.verbose:
logger.setLevel(logging.DEBUG)
logger.info(f"Simulating '{args.model_name}' "
f"({args.model_directory}) using PEtab data from "
f"{args.yaml_file_name}")
# load PEtab files
problem = petab.Problem.from_yaml(args.yaml_file_name)
petab.flatten_timepoint_specific_output_overrides(problem)
# load model
if args.model_directory:
sys.path.insert(0, args.model_directory)
model_module = importlib.import_module(args.model_name)
amici_model = model_module.getModel()
res = simulate_petab(petab_problem=problem,
amici_model=amici_model,
log_level=logging.DEBUG)
rdatas = res[RDATAS]
llh = res[LLH]
# create simulation PEtab table
sim_df = rdatas_to_measurement_df(rdatas=rdatas,
model=amici_model,
measurement_df=problem.measurement_df)
sim_df.rename(columns={petab.MEASUREMENT: petab.SIMULATION}, inplace=True)
if args.simulation_file:
sim_df.to_csv(index=False, sep="\t")
if args.plot:
try:
# visualize fit
axs = plot_petab_problem(petab_problem=problem, sim_data=sim_df)
# save figure
for plot_id, ax in axs.items():
fig_path = os.path.join(
args.model_directory,
args.model_name + "_" + plot_id + "_vis.png")
logger.info(f"Saving figure to {fig_path}")
ax.get_figure().savefig(fig_path, dpi=150)
except NotImplementedError:
pass
if args.check:
references_yaml = os.path.join(os.path.dirname(__file__),
"benchmark_models.yaml")
with open(references_yaml) as f:
refs = yaml.full_load(f)
try:
ref_llh = refs[args.model_name]["llh"]
logger.info(f"Reference llh: {ref_llh}")
if abs(ref_llh - llh) < 1e-3:
logger.info(f"Computed llh {llh} matches reference "
f"{ref_llh}. Absolute difference is "
f"{ref_llh - llh}.")
else:
logger.error(f"Computed llh {llh} does not match reference "
f"{ref_llh}. Absolute difference is "
f"{ref_llh - llh}."
f" Relative difference is {llh / ref_llh}")
sys.exit(1)
except KeyError:
logger.error("No reference likelihood found for "
f"{args.model_name} in {references_yaml}")